// This program converts a set of images to a lmdb/leveldb by storing them
// as Datum proto buffers.
// Usage:
//   convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE IM_DB_NAME LABEL_DB_NAME LABEL_COUNT
//
// where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
// should be a list of files as well as their labels, in the format as
//   subfolder1/file1.JPEG 7
//   ....



//#ifdef    MULTILABEL



#include <algorithm>
#include <fstream>  // NOLINT(readability/streams)
#include <string>
#include <utility>
#include <vector>

#include "boost/scoped_ptr.hpp"
#include "gflags/gflags.h"
#include "glog/logging.h"

#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
#include "caffe/util/format.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/rng.hpp"

using namespace caffe;  // NOLINT(build/namespaces)
using std::pair;
using boost::scoped_ptr;

DEFINE_bool(gray, false,
    "When this option is on, treat images as grayscale ones");
DEFINE_bool(shuffle, false,
    "Randomly shuffle the order of images and their labels");
DEFINE_string(backend, "lmdb",
    "The backend {lmdb, leveldb} for storing the result");
DEFINE_int32(resize_width, 0, "Width images are resized to");
DEFINE_int32(resize_height, 0, "Height images are resized to");
DEFINE_bool(check_size, false,
    "When this option is on, check that all the datum have the same size");
DEFINE_bool(encoded, false,
    "When this option is on, the encoded image will be save in datum");
DEFINE_string(encode_type, "",
    "Optional: What type should we encode the image as ('png','jpg',...).");

int main(int argc, char** argv) {
#ifdef USE_OPENCV
    ::google::InitGoogleLogging(argv[0]);
    // Print output to stderr (while still logging)
    FLAGS_alsologtostderr = 1;

#ifndef GFLAGS_GFLAGS_H_
    namespace gflags = google;
#endif

    gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb\n"
        "format used as input for Caffe.\n"
        "Usage:\n"
        "    convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME\n"
        "The ImageNet dataset for the training demo is at\n"
        "    http://www.image-net.org/download-images\n");
    gflags::ParseCommandLineFlags(&argc, &argv, true);

    if (argc < 6) {
        gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/convert_imageset");
        return 1;
    }

    const bool is_color = !FLAGS_gray;
    const bool check_size = FLAGS_check_size;
    const bool encoded = FLAGS_encoded;
    const string encode_type = FLAGS_encode_type;

    std::ifstream infile(argv[2]);

    LOG(INFO) << "argv[2] " << argv[2];
    
    std::vector<std::pair<std::string, std::vector<float> > > lines;
    std::string filename;

    std::string label_count_string = argv[5] ;
    int label_count = std::atoi(label_count_string.c_str());
    LOG(INFO) << "argv[5] " << label_count ; 
    std::vector<float> label(label_count);

    while (infile >> filename)
    {
        for (int i = 0; i < label_count;i++)
        {
            infile >> label[i];
        }

        lines.push_back(std::make_pair(filename , label));
    }

    LOG(INFO) << "Before shuffle data a total of " << lines.size() << "images.";

    if (FLAGS_shuffle) {

        // randomly shuffle data
        LOG(INFO) << "Shuffling data";
        shuffle(lines.begin(), lines.end());
    }
    LOG(INFO) << "A total of " << lines.size() << " images.";

    if (encode_type.size() && !encoded)
        LOG(INFO) << "encode_type specified, assuming encoded=true.";

    int resize_height = std::max<int>(0, FLAGS_resize_height);
    int resize_width = std::max<int>(0, FLAGS_resize_width);

    // Create new DB
    scoped_ptr<db::DB> db_image(db::GetDB(FLAGS_backend));
    scoped_ptr<db::DB> db_label(db::GetDB(FLAGS_backend));
    db_image->Open(argv[3], db::NEW);
    db_label->Open(argv[4], db::NEW);
    scoped_ptr<db::Transaction> txn_image(db_image->NewTransaction());
    scoped_ptr<db::Transaction> txn_label(db_label->NewTransaction());

    // Storing to db
    std::string root_folder(argv[1]);
    Datum datum_label;
    Datum datum_image;
    int count = 0;
    int data_size_label = 0;
    int data_size_image = 0;
    bool data_size_initialized = false;

    for (int line_id = 0; line_id < lines.size(); ++line_id) {
        bool status;
        std::string enc = encode_type;
        if (encoded && !enc.size()) {
            // Guess the encoding type from the file name
            string fn = lines[line_id].first;
            size_t p = fn.rfind('.');
            if (p == fn.npos)
                LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
            enc = fn.substr(p);
            std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
        }

        status = ReadImageToDatum(root_folder + lines[line_id].first,
            lines[line_id].second[0], resize_height, resize_width, is_color,
            enc, &datum_image);
        if (status == false) continue;

        datum_label.set_height(1);
        datum_label.set_width(1);
        datum_label.set_channels(label_count);
        int count_tmp = datum_label.float_data_size();
        for (int index_label = 0; index_label < lines[line_id].second.size(); index_label++)
        {
            float tmp_float_value = lines[line_id].second[index_label];
            datum_label.add_float_data(tmp_float_value);
        }

        if (check_size) {
            if (!data_size_initialized) {
                data_size_label = datum_label.channels() * datum_label.height() * datum_label.width();
                data_size_image = datum_image.channels() * datum_image.height() * datum_image.width();
                data_size_initialized = true;
            }
            else {
                const std::string& data_label = datum_label.data();
                CHECK_EQ(data_label.size(), data_size_label) << "Incorrect data field size "
                    << data_label.size();

                const std::string& data_image = data_image.data();
                CHECK_EQ(data_image.size(), data_size_image) << "Incorrect data field size "
                    << data_image.size();
            }
        }
        // sequential
        string key_str_image = caffe::format_int(line_id, 8) + "_" + lines[line_id].first;
        string key_str_label = caffe::format_int(line_id, 8) + "label_" + lines[line_id].first;

        // Put in db
        string out_label;
        string out_image;
        CHECK(datum_label.SerializeToString(&out_label));
        CHECK(datum_image.SerializeToString(&out_image));

        datum_label.clear_float_data();
        txn_label->Put(key_str_label, out_label);
        txn_image->Put(key_str_image, out_image);
        if (++count % 1000 == 0) {
            // Commit db
            txn_image->Commit();
            txn_image.reset(db_image->NewTransaction());

            txn_label->Commit();
            txn_label.reset(db_label->NewTransaction());
            LOG(INFO) << "Processed " << count << " files.";
        }

    }
    // write the last batch
    if (count % 1000 != 0) {
        txn_label->Commit();
        txn_image->Commit();
        LOG(INFO) << "Processed " << count << " files.";
    }
#else
    LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
#endif  // USE_OPENCV
    return 0;
}


//#endif

